Enhanced Detection Rate of AI for Lung Cancer Detection on GP-Referred Chest X-rays: A Real-World Retrospective Evaluation

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Abstract

ObjectivesTo assess whether an artificial intelligence (AI) chest radiograph (CXR) tool could enhance lung cancer detection on primary care-referred CXRs in the UK, and to estimate the magnitude of any improvement. MethodsFrom [~]280,000 primary care-referred CXRs, we identified 1,600 linked to a lung cancer diagnosis (ICD-10 C34) within six months. Missed lung cancers were defined by review of the CXR report and comparison of diagnostic CT and positron emission tomography (PET) imaging with the index CXR by three specialist radiology clinicians. CXRs with a retrospectively visible but initially missed cancer were re-analysed using a commercially available AI tool. The primary outcome was the enhanced detection rate (EDR), defined as the proportion of confirmed cancers missed on CXR but correctly identified by AI. ResultsOf 1,600 CXRs, 105 (6.6%) contained a retrospectively visible cancer that had been missed at first report. AI flagged abnormalities in 72/105 (69%) and delineated the primary tumour in 38/105 (36%). This equated to an absolute EDR of 2.4% and a relative EDR of 2.9%. Missed lesions were concentrated in central and upper zones, whereas AI detections were more frequent in peripheral locations. ConclusionsAI identified over one-third of retrospectively visible lung cancers that were missed at initial CXR reporting. Implementation as decision support could provide a modest but potentially meaningful increase in lung cancer detection in primary care. Advances in knowledgeIn this large real-world UK study, AI modestly improved lung cancer detection on CXR, with complementary detection patterns to human readers, but performance remained limited in anatomically complex regions.

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